Although FL can preserve privacy for users in AI training, data features embedded in model updates can be leaked which thus can reveal private user information [1]}. To overcome this challenge, a differential privacy technique is adopted by injecting artificial noise into the local gradient training in each communication round. Then, an incentive mechanism is integrated to attract more UAVs in the AI training to improve the overall FL performance. This approach is validated by a simulation based on the MNIST dataset including 60,000 training samples and 10,000 test samples with convolutional neural networks
as the learning model. Evaluation results confirm a high utility of UAVs and low aggregation error while reducing convergence latency. Further, an incentive-based crowdsensing/crowdsourcing architecture is designed in [2]} based on FL for IoT in edge computing. Each client can receive an incentive after successfully uploading its computed parameter to the MEC server, in order to attract more users to contribute computational resources for running AI models.
This incentive process is modeled by a two-stage Stackelberg game that shows advantages over the heuristic approach in terms of a utility gain enhancement by 22% for different system settings.
Another edge crowdsourcing scheme is investigated in [3]} based on an integrated FL-blockchain architecture in IoT environments. More specifically, a hierarchical crowdsourcing-empowered FL system is built for enabling distributed ML training at the network edge. The ultimate goal is to optimize the service quality of appliance manufacturers while preserving privacy with different privacy techniques. For security guarantees, blockchain is integrated with FL training to detect and prevent malicious attacks from modifying gradient updates and audit the parameter update of FL clients such as IoT devices. Implementation results prove the feasibility of the proposed FLchain with high training accuracy, security protection, and low system latency.
